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dl-tutorials

This repository contains demonstrations done with deep learning computer vision models.

https://github.com/lapix-ufsc/dl-tutorials

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
  • ○
    Academic publication links
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    Academic email domains
  • ○
    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.0%) to scientific vocabulary

Keywords

computer-vision deep-learning image-classfication instance-segmentation machine-learning object-detection panoptic-segmentation semantic-segmentation
Last synced: 6 months ago · JSON representation ·

Repository

This repository contains demonstrations done with deep learning computer vision models.

Basic Info
Statistics
  • Stars: 6
  • Watchers: 2
  • Forks: 1
  • Open Issues: 4
  • Releases: 1
Topics
computer-vision deep-learning image-classfication instance-segmentation machine-learning object-detection panoptic-segmentation semantic-segmentation
Created over 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

Deep Learning tutorials

Content

This package/repository is not a library, but a set of tutorials using several libraries with models and utilities for computer vision using deep learning.

These tutorials were built in a way that their contents are self-contained, and that they can be used as a basis for other experiments.

The tutorials will cover models and tools for semantic segmentation, object detection, image classification, tracking, augmentation, model evaluation, among other topics.

Datasets

These tutorials are the fruit of different Lapix researchers, who throughout their masters or doctoral degrees developed several computer vision datasets.

Therefore, these tutorials were created from experiments using the following datasets:

  1. CCAgT: Images of Cervical Cells with AgNOR Stain Technique
  2. Clouds-1000
  3. UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4)

Authors

| Name | GitHub | Orcid | |:----------------------------:|--------------------------------------------------|--------------------------------------------------------------| | Aldo von Wangenheim | @awangenh | 0000-0003-4532-1417 | | João Gustavo Atkinson Amorim | @johnnv1 | 0000-0003-3361-6891 | | André Victória Matias | @andrevmatias | 0000-0003-0268-0233 |

Owner

  • Name: Image Processing and Computer Graphics Lab - LAPiX
  • Login: lapix-ufsc
  • Kind: organization
  • Location: Florianópolis - SC, Brazil

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Atkinson Amorim"
  given-names: "João Gustavo"
  orcid: "https://orcid.org/0000-0003-3361-6891"
- family-names: "Victória Matias"
  given-names: "André"
  orcid: "https://orcid.org/0000-0003-0268-0233"
- family-names: "von Wangenheim"
  given-names: "Aldo"
  orcid: "https://orcid.org/0000-0003-4532-1417"
title: "Lapix - Deep Learning Tutorials"
version: "Update to the used version here"
doi: 10.5281/zenodo.6967940
url: "https://doi.org/10.5281/zenodo.6967940"

GitHub Events

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  • Delete event: 1
  • Push event: 3
  • Pull request event: 1
  • Pull request review event: 1
  • Create event: 1
Last Year
  • Delete event: 1
  • Push event: 3
  • Pull request event: 1
  • Pull request review event: 1
  • Create event: 1

Dependencies

.github/workflows/deploy.yaml actions
  • actions/checkout v3 composite
  • peaceiris/actions-gh-pages v3 composite
.github/workflows/doc-build.yaml actions
  • actions/checkout v3 composite